Improve SNR on masked fMRI signals.
This function can do several things on the input signals, in the following order: - detrend - standardize - remove confounds - low- and high-pass filter
Low-pass filtering improves specificity.
High-pass filtering should be kept small, to keep some sensitivity.
Filtering is only meaningful on evenly-sampled signals.
Parameters : | signals: numpy.ndarray :
confounds: numpy.ndarray, str or list of :
t_r: float :
low_pass, high_pass: float :
detrend: bool :
standardize: bool :
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Returns : | cleaned_signals: numpy.ndarray :
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Notes
Confounds removal is based on a projection on the orthogonal of the signal space. See Friston, K. J., A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, et R. S. J. Frackowiak. “Statistical Parametric Maps in Functional Imaging: A General Linear Approach”. Human Brain Mapping 2, no 4 (1994): 189-210.